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Full-Text Articles in Physical Sciences and Mathematics

Overcoming Uncertainties In Molecular Visualization, Thomas Wischgoll Feb 2023

Overcoming Uncertainties In Molecular Visualization, Thomas Wischgoll

Computer Science and Engineering Faculty Publications

Uncertainties are difficult if not impossible to avoid. Capturing data from the analog world almost always results in some form of uncertainty. The amount of uncertainty depends on the method of measurement and its accuracy. When visualizing data that has some associated uncertainty, it is essential to properly process and convey such uncertainty and especially the amount of uncertainty keeping in mind that additional processing steps can amplify the uncertainty. There are various sources of uncertainty, such as numerical limitations or limitations of the capture device. However, there are other sources of uncertainty. Some of these uncertainties stem from model …

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Wright State University Fact Sheet, 2022-2023, Office Of Institutional Research & Effectiveness, Wright State University Jan 2023

Wright State University Fact Sheet, 2022-2023, Office Of Institutional Research & Effectiveness, Wright State University

Wright State University Fact Sheets

The Wright State University Fact Sheet showcasing numbers and statistics for Wright State University including demographics, funding, programs, and employment for the 2022-2023 academic year.

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Application Of The Hvsr Technique To Map The Depth And Elevation Of The Bedrock Underlying Wright State University Campus, Dayton, Ohio, Devika L. Ghuge Jan 2023

Application Of The Hvsr Technique To Map The Depth And Elevation Of The Bedrock Underlying Wright State University Campus, Dayton, Ohio, Devika L. Ghuge

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Estimating sediment thickness and bedrock surface geometry is critical for many hydrogeologic studies. The horizontal-to-vertical spectral ratio (HVSR), a passive seismic method is a unique, non-invasive technique for speedily estimating bedrock depth. To record ambient seismic noise, the H/V method employs a single broadband three-component seismometer. A field assessment was conducted on the Wright State University Campus in Dayton, Ohio, to determine the depth (z) and elevation of the bedrock. Data were collected at 60 different locations. A known value for the depth of bedrock on campus was determined using the log from a local water well available from the …

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Encryption And Compression Classification Of Internet Of Things Traffic, Mariam Najdat M Saleh Jan 2023

Encryption And Compression Classification Of Internet Of Things Traffic, Mariam Najdat M Saleh

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The Internet of Things (IoT) is used in many fields that generate sensitive data, such as healthcare and surveillance. Increased reliance on IoT raised serious information security concerns. This dissertation presents three systems for analyzing and classifying IoT traffic using Deep Learning (DL) models, and a large dataset is built for systems training and evaluation. The first system studies the effect of combining raw data and engineered features to optimize the classification of encrypted and compressed IoT traffic using Engineered Features Classification (EFC), Raw Data Classification (RDC), and combined Raw Data and Engineered Features Classification (RDEFC) approaches. Our results demonstrate …

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Efficient Cloud-Based Ml-Approach For Safe Smart Cities, Niveshitha Niveshitha Jan 2023

Efficient Cloud-Based Ml-Approach For Safe Smart Cities, Niveshitha Niveshitha

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Smart cities have emerged to tackle many critical problems that can thwart the overwhelming urbanization process, such as traffic jams, environmental pollution, expensive health care, and increasing energy demand. This Master thesis proposes efficient and high-quality cloud-based machine-learning solutions for efficient and sustainable smart cities environment. Different supervised machine-learning models for air quality predication (AQP) in efficient and sustainable smart cities environment is developed. For that, ML-based techniques are implemented using cloud-based solutions. For example, regression and classification methods are implemented using distributed cloud computing to forecast air execution time and accuracy of the implemented ML solution. These models are …

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Effects Of Phosphorus-Binding Agents On Nutrient Dynamics And A Planktothrix Bloom In A Shallow, Semi-Enclosed Lake Area, Joseph Lee Davidson Jan 2023

Effects Of Phosphorus-Binding Agents On Nutrient Dynamics And A Planktothrix Bloom In A Shallow, Semi-Enclosed Lake Area, Joseph Lee Davidson

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Grand Lake St. Marys is the largest (52 km2) inland lake in Ohio, USA, and receives high nutrient loadings (90th percentile for total nitrogen (N) and phosphorus (P) concentrations in the USA) from a watershed dominated by agricultural row-crops and livestock production. Eutrophication has led to cyanobacterial harmful algal blooms, dominated by non-N2 fixing Planktothrix, that persist year-round, including in winter months. In summer 2020 and 2021, multiple treatments using P-binding agents within a 3.5 ha swimming enclosure were conducted to remove excess dissolved P from the water column. The objective of this study was to examine pre-and-post treatment biogeochemical …

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Contributors To Pathologic Depolarization In Myotonia Congenita, Jessica Hope Myers Jan 2023

Contributors To Pathologic Depolarization In Myotonia Congenita, Jessica Hope Myers

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Myotonia congenita is an inherited skeletal muscle disorder caused by loss-of-function mutation in the CLCN1 gene. This gene encodes the ClC-1 chloride channel, which is almost exclusively expressed in skeletal muscle where it acts to stabilize the resting membrane potential. Loss of this chloride channel leads to skeletal muscle hyperexcitability, resulting in involuntary muscle action potentials (myotonic discharges) seen clinically as muscle stiffness (myotonia). Stiffness affects the limb and facial muscles, though specific muscle involvement can vary between patients. Interestingly, respiratory distress is not part of this disease despite muscles of respiration such as the diaphragm muscle also carrying this …

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Green Synthesis Of Nanoparticles And Their Utilization In Electrochemical Detection Of Catechol- Or Phenol-Based Substances, Daniel Laurence Myers Iii Jan 2023

Green Synthesis Of Nanoparticles And Their Utilization In Electrochemical Detection Of Catechol- Or Phenol-Based Substances, Daniel Laurence Myers Iii

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PART A: GREEN SYNTHESIS OF COPPER NANOPARTICLES AND THEIR UTILIZATION IN THE DETECTION OF NEUROTRANSMITTERS Neurotransmitters, such as dopamine and epinephrine, are chemicals frequently found in the brain and are responsible for a number of human moods, needs, and emotions.1, 2 Detection of such neurotransmitters allows for a better judgement of a person’s physical and mental state, a utility that is vital in determining the presence of disease and mental illnesses.2 Electrochemical detectors used for such detections are often modified by materials such as metal nanoparticles.3 However, synthesizing nanoparticles can involve or produce chemicals, ammonium hydroxide among them, that are …

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Solidity Compiler Version Identification On Smart Contract Bytecode, Lakshmi Prasanna Katyayani Devasani Jan 2023

Solidity Compiler Version Identification On Smart Contract Bytecode, Lakshmi Prasanna Katyayani Devasani

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Identifying the version of the Solidity compiler used to create an Ethereum contract is a challenging task, especially when the contract bytecode is obfuscated and lacks explicit metadata. Ethereum bytecode is highly complex, as it is generated by the Solidity compiler, which translates high-level programming constructs into low-level, stack-based code. Additionally, the Solidity compiler undergoes frequent updates and modifications, resulting in continuous evolution of bytecode patterns. To address this challenge, we propose using deep learning models to analyze Ethereum bytecodes and infer the compiler version that produced them. A large number of Ethereum contracts and the corresponding compiler versions is …

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The Open Charge Point Protocol (Ocpp) Version 1.6 Cyber Range A Training And Testing Platform, David Elmo Ii Jan 2023

The Open Charge Point Protocol (Ocpp) Version 1.6 Cyber Range A Training And Testing Platform, David Elmo Ii

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The widespread expansion of Electric Vehicles (EV) throughout the world creates a requirement for charging stations. While Cybersecurity research is rapidly expanding in the field of Electric Vehicle Infrastructure, efforts are impacted by the availability of testing platforms. This paper presents a solution called the “Open Charge Point Protocol (OCPP) Cyber Range.” Its purpose is to conduct Cybersecurity research against vulnerabilities in the OCPP v1.6 protocol. The OCPP Cyber Range can be used to enable current or future research and to train operators and system managers of Electric Charge Vehicle Supply Equipment (EVSE). This paper demonstrates this solution using three …

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A Secure And Efficient Iiot Anomaly Detection Approach Using A Hybrid Deep Learning Technique, Bharath Reedy Konatham Jan 2023

A Secure And Efficient Iiot Anomaly Detection Approach Using A Hybrid Deep Learning Technique, Bharath Reedy Konatham

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The Industrial Internet of Things (IIoT) refers to a set of smart devices, i.e., actuators, detectors, smart sensors, and autonomous systems connected throughout the Internet to help achieve the purpose of various industrial applications. Unfortunately, IIoT applications are increasingly integrated into insecure physical environments leading to greater exposure to new cyber and physical system attacks. In the current IIoT security realm, effective anomaly detection is crucial for ensuring the integrity and reliability of critical infrastructure. Traditional security solutions may not apply to IIoT due to new dimensions, including extreme energy constraints in IIoT devices. Deep learning (DL) techniques like Convolutional …

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Merit Study Of Battery Or Hydrogen Energy Storage For Large Scale, Combined Wind And Solar Electricity Generation, Ashley K. Moore Jan 2023

Merit Study Of Battery Or Hydrogen Energy Storage For Large Scale, Combined Wind And Solar Electricity Generation, Ashley K. Moore

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In the past several years, the energy sector has experienced a rapid increase in renewable energy installations due to declining capital costs for wind turbines, solar panels, and batteries. Wind and solar electricity generation are intermittent in nature which must be considered in an economic analysis if a fair comparison is to be made between electricity supplied from renewables and electricity purchased from the grid. Energy storage reduces curtailment of wind and solar and minimizes electricity purchases from the grid by storing excess electricity and deploying the energy at times when demand exceeds the renewable energy supply. The objective of …

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A Formal Framework For Disaster Risk Properties, Shirly Stephen, Mark Schildhauer, Kitty Currier, Pascal Hitzler, Cogan Shimizu, Krzysztof Janowicz, Dean Rehberger Jan 2023

A Formal Framework For Disaster Risk Properties, Shirly Stephen, Mark Schildhauer, Kitty Currier, Pascal Hitzler, Cogan Shimizu, Krzysztof Janowicz, Dean Rehberger

Computer Science and Engineering Faculty Publications

Disaster risk properties (or disaster variables) such as intensity, exposure, severity, vulnerability, resilience, and capacity are significant because they provide essential information for understanding and managing disaster risk and cascading effects. While there are an increasing number of datasets that record these properties based on different criteria, such as regional levels (e.g., community resilience at counties vs. census tracts), thematic levels (e.g., social vulnerability based on race vs. socioeconomic status), or even for different hazard types (e.g., disaster risk for earthquakes vs. hurricanes), we lack a formal model that captures the semantics of these properties, i.e., their interactions with one …

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A Pattern For Modeling Computational Observations, Cogan Shimizu, Pascal Hitzler, Charles F. Vardeman Jan 2023

A Pattern For Modeling Computational Observations, Cogan Shimizu, Pascal Hitzler, Charles F. Vardeman

Computer Science and Engineering Faculty Publications

Knowledge graphs (KG) are an established method for heterogeneous data integration and have begun powering complex software agents. However, it is important to understand where the data in the knowledge graph originates, especially within the context of synthetic research agents and other trustworthy AI systems. In this paper, we propose an ontology design pattern for tracking the provenance and context of computational observations, as well as a proposing a supporting, simplified conceptual framework for modeling abstract and concrete versions of the same underlying notion.

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Besoins Ontologiques Pour La Transformation Des Aliments, D. Dooley, M. Weber, L. Ibanescu, M. Lange, L. Chan, L. Soldatova, C. Yang, R. Warren, Cogan Shimizu, H. Mcginty, W. Hsiao Jan 2023

Besoins Ontologiques Pour La Transformation Des Aliments, D. Dooley, M. Weber, L. Ibanescu, M. Lange, L. Chan, L. Soldatova, C. Yang, R. Warren, Cogan Shimizu, H. Mcginty, W. Hsiao

Computer Science and Engineering Faculty Publications

People often value the sensual, celebratory, and health aspects of food, but behind this experience exists many other value-laden agricultural production, distribution, manufacturing, and physiological processes that support or undermine a healthy population. The complexity of such processes is evident in both every-day food preparation of recipes and in industrial food manufacturing, packaging and storage. An integrated ontology landscape does not yet exist to cover all the entities at work in this farm to fork journey. It seems necessary to construct such a vision by reusing expert-curated fit-to-purpose ontology subdomains. The challenge is to make this merger be, by analogy, …

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The Knowwheregraph Ontology: A Showcase, Cogan Shimizu, Shirly Stephen, Rui Zhu, Kitty Currier, Mark Schildhauer, Dean Rehberger, Pascal Hitzler, Krzysztof Janowicz, Colby K. Fisher, Mohammad Saeid Mahdavinejad, Antrea Christou, Adrita Barua, Abhilekha Dalal, Sanaz Saki Norouzi, Zilong Liu, Meilin Shi, Ling Cai, Gengchen Mai, Zhangyu Wang, Yuanyuan Tian Jan 2023

The Knowwheregraph Ontology: A Showcase, Cogan Shimizu, Shirly Stephen, Rui Zhu, Kitty Currier, Mark Schildhauer, Dean Rehberger, Pascal Hitzler, Krzysztof Janowicz, Colby K. Fisher, Mohammad Saeid Mahdavinejad, Antrea Christou, Adrita Barua, Abhilekha Dalal, Sanaz Saki Norouzi, Zilong Liu, Meilin Shi, Ling Cai, Gengchen Mai, Zhangyu Wang, Yuanyuan Tian

Computer Science and Engineering Faculty Publications

KnowWhereGraph is one of the largest fully publicly available spatially enabled knowledge graphs. It includes data on natural hazards (e.g., hurricanes, wildfires), climate variables (e.g., air temperature, precipitation), soil properties, crop and land-cover types, demographics, and human health, among other themes. These have been leveraged through the graph by a variety of applications to address challenges in food security and agricultural supply chains; sustainability related to soil conservation practices and farm labor; and delivery of emergency humanitarian aid following a disaster. This paper showcases the KnowWhereGraph ontology, which acts as the schema for the KnowWhereGraph. We discuss how it enables …

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Graphs Without A 2c3-Minor And Bicircular Matroids Without A U3,6-Minor, Daniel Slilaty Jan 2023

Graphs Without A 2c3-Minor And Bicircular Matroids Without A U3,6-Minor, Daniel Slilaty

Mathematics and Statistics Faculty Publications

In this note we characterize all graphs without a 2C3-minor. A consequence of this result is a characterization of the bicircular matroids with no U3,6-minor.

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Direct Parameter Fitting Of Action Potentials In Skeletal Muscle Cells Which Include Longitudinal Segments, Tyme Suda Jan 2023

Direct Parameter Fitting Of Action Potentials In Skeletal Muscle Cells Which Include Longitudinal Segments, Tyme Suda

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Excitation of skeletal muscle cells triggers a large voltage spike known as an action potential (AP), leading to muscle contraction. Modeling of an AP is typically done using the method developed by scientists Hodgkin and Huxley (HH). In the HH method, voltage and time gated Na+ and K+ ionic currents are simulated, along with a positive “Leak” ionic current and capacitive current. Due to the complexity and the computational time required for simulation, direct fitting of HH parameters to experimental APs has rarely been attempted. A previous thesis at Wright State performed direct fitting for the case of a single …

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Data-Driven Strategies For Pain Management In Patients With Sickle Cell Disease, Swati Padhee Jan 2023

Data-Driven Strategies For Pain Management In Patients With Sickle Cell Disease, Swati Padhee

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This research explores data-driven AI techniques to extract insights from relevant medical data for pain management in patients with Sickle Cell Disease (SCD). SCD is an inherited red blood cell disorder that can cause a multitude of complications throughout an individual’s life. Most patients with SCD experience repeated, unpredictable episodes of severe pain. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting the patient’s pain intensity level due to the subjective nature of pain. In this study, we leverage multiple data-driven AI techniques to improve pain management in patients with SCD. The proposed approaches …

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A Novel Knowledge-Based Federated Deep Learning Approach For Enhancing Security And Privacy Preservation In Iot Edge Computing Applications, Tabassum Simra Jan 2023

A Novel Knowledge-Based Federated Deep Learning Approach For Enhancing Security And Privacy Preservation In Iot Edge Computing Applications, Tabassum Simra

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The Internet of Things (IoT) infrastructure encompasses smart devices and real-time sensors connected through the Internet, facilitating the exchange of large datasets among these devices. This interconnected network of IoT sensors generates a significant volume of data for processing and analysis by embedded IoT Edge Computing systems. IoT Edge Computing systems enable efficient real-time analysis and data communications. Furthermore, IoT Edge Computing emerges to enhance the overall efficiency of IoT applications, making them adept at handling the dynamic demands of AI-based and large data-driven applications. The integration of IoT Edge Computing introduces several unique research challenges. Unfortunately, IoT Edge Computing …

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A First Study Of The Effect Of Surface Roughness In Additive-Manufactured Copper At Thz Frequencies, Tomas Di Fulvio, Elliott R. Brown, Andrew J. Huebner, Michael A. Saville, Paul Sotirelis Jan 2023

A First Study Of The Effect Of Surface Roughness In Additive-Manufactured Copper At Thz Frequencies, Tomas Di Fulvio, Elliott R. Brown, Andrew J. Huebner, Michael A. Saville, Paul Sotirelis

Physics Faculty Publications

The surface roughness of additive-manufactured copper is characterized by profilometry, and its effect on the THz reflectance is measured at 600 GHz. A strong specular component is measured, as well as a non-specular component displaying fluctuations in the wings of the angular reflectance plot.

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Code Execution Capability As A Metric For Machine Learning–Assisted Software Vulnerability Detection Models, Daniel Grahn, Lingwei Chen, Junjie Zhang Jan 2023

Code Execution Capability As A Metric For Machine Learning–Assisted Software Vulnerability Detection Models, Daniel Grahn, Lingwei Chen, Junjie Zhang

Computer Science and Engineering Faculty Publications

In this paper, we consider how the ability to learn Code Execution Tasks affects a model’s accuracy on software vulnerability detection (SVD) benchmark datasets. We initially find that models can achieve near state-of-the-art accuracy on SVD benchmarks regardless of their ability to learn Code Execution Tasks. However, these models fail to generalize well across SVD benchmarks. The results indicate a bias in the datasets that allows models to predict non- SVD signals. Under the theory that different collection methods will reduce biases, we investigate combining the SVD datasets. When trained on combined datasets, SVD accuracy is reduced but correlation with …

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A Pattern For Representing Scientific Taxonomies, Shirly Stephen, Cogan Shimizu, Mark Schildhauer, Rui Zhu, Krzysztof Janowicz, Pascal Hitzler Jan 2023

A Pattern For Representing Scientific Taxonomies, Shirly Stephen, Cogan Shimizu, Mark Schildhauer, Rui Zhu, Krzysztof Janowicz, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Standard taxonomies that are meant to serve as reference specifications of specific scientific domains are developed through extensive review by scientists and experts. For data integration and interoperability needs within Knowledge Graphs (KGs), these taxonomies must be translated into formal ontologies. In this paper we present an ontology design pattern for modeling a scientific taxonomy as an ontology. The focus of the pattern is to 1) capture temporal dynamics of concepts as taxonomies evolve, 2) model the provenance of concepts to add context and enable governance, 3) assist the translation of taxonomic relations to ontological relations appropriately that will empower …

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The Expertise Ontology: Modeling Expertise In The Context Of Emergency Management, Shirly Stephen, Mark Schildhauer, Ling Cai, Yuanyuan Tian, Kitty Currier, Cogan Shimizu, Krzysztof Janowicz, Pascal Hitzler, Anna Lopez-Carr, Andrew Schroeder, Zilong Liu, Rui Zhu, Dean Rehberger, Colby K. Fisher, Gengchen Mai Jan 2023

The Expertise Ontology: Modeling Expertise In The Context Of Emergency Management, Shirly Stephen, Mark Schildhauer, Ling Cai, Yuanyuan Tian, Kitty Currier, Cogan Shimizu, Krzysztof Janowicz, Pascal Hitzler, Anna Lopez-Carr, Andrew Schroeder, Zilong Liu, Rui Zhu, Dean Rehberger, Colby K. Fisher, Gengchen Mai

Computer Science and Engineering Faculty Publications

It is crucial for emergency management organizations to have rapid access to relevant experts who can advise and assist following a disaster. To improve expert-mining and recommendation capabilities, creating a knowledge graph that links experts to their corresponding topics of expertise and other sources of relevant information is a natural choice to capture an integrated network of people and a rich taxonomy of expertise. In this paper, we present an ontology for modeling experts, their expertise topics and relations between them, and their spatiotemporal scoping. We go on to discuss the primary conceptual components and how they can be instantiated, …

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Festival Of Research Abstracts, 2023, College Of Science And Mathematics, Wright State University Jan 2023

Festival Of Research Abstracts, 2023, College Of Science And Mathematics, Wright State University

Festival of Research

The collection of abstracts accepted for the 2023 Festival of Research hosted by the Wright State University College of Science and Mathematics.

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Odd Solutions To Systems Of Inequalities Coming From Regular Chain Groups, Daniel Slilaty Jan 2023

Odd Solutions To Systems Of Inequalities Coming From Regular Chain Groups, Daniel Slilaty

Mathematics and Statistics Faculty Publications

Hoffman’s theorem on feasible circulations and Ghouila-Houry’s theorem on feasible tensions are classical results of graph theory. Camion generalized these results to systems of inequalities over regular chain groups. An analogue of Camion’s result is proved in which solutions can be forced to be odd valued. The obtained result also generalizes the results of Pretzel and Youngs as well as Slilaty. It is also shown how Ghouila-Houry’s result can be used to give a new proof of the graph- coloring theorem of Minty and Vitaver.

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Accelerating Precision Station Keeping For Automated Aircraft, James D. Anderson Jan 2023

Accelerating Precision Station Keeping For Automated Aircraft, James D. Anderson

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Automated vehicles pose challenges in various research domains, including robotics, machine learning, computer vision, public safety, system certification, and beyond. These vehicles autonomously handle navigation and locomotion, often requiring minimal user interaction, and can operate on land, in water, or in the air. In the context of aircraft, one specific application is Automated Aerial Refueling (AAR). Traditional aerial refueling involves a "tanker" aircraft using a mechanism, such as a rigid boom arm or a flexible hose, to transfer fuel to another aircraft designated as the "receiver". For AAR, the boom arm may be maneuvered automatically, or in certain instances the …

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Comparative Adjudication Of Noisy And Subjective Data Annotation Disagreements For Deep Learning, Scott David Williams Jan 2023

Comparative Adjudication Of Noisy And Subjective Data Annotation Disagreements For Deep Learning, Scott David Williams

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Obtaining accurate inferences from deep neural networks is difficult when models are trained on instances with conflicting labels. Algorithmic recognition of online hate speech illustrates this. No human annotator is perfectly reliable, so multiple annotators evaluate and label online posts in a corpus. Labeling scheme limitations, differences in annotators' beliefs, and limits to annotators' honesty and carefulness cause some labels to disagree. Consequently, decisive and accurate inferences become less likely. Some practical applications such as social research can tolerate some indecisiveness. However, an online platform using an indecisive classifier for automated content moderation could create more problems than it solves. …

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Enhancing Graph Convolutional Network With Label Propagation And Residual For Malware Detection, Aravinda Sai Gundubogula Jan 2023

Enhancing Graph Convolutional Network With Label Propagation And Residual For Malware Detection, Aravinda Sai Gundubogula

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Malware detection is a critical task in ensuring the security of computer systems. Due to a surge in malware and the malware program sophistication, machine learning methods have been developed to perform such a task with great success. To further learn structural semantics, Graph Neural Networks abbreviated as GNNs have emerged as a recent practice for malware detection by modeling the relationships between various components of a program as a graph, which deliver promising detection performance improvement. However, this line of research attends to individual programs while overlooking program interactions; also, these GNNs tend to perform feature aggregation from neighbors …

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Effective Systems For Insider Threat Detection, Muhanned Qasim Jabbar Alslaiman Jan 2023

Effective Systems For Insider Threat Detection, Muhanned Qasim Jabbar Alslaiman

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Insider threats to information security have become a burden for organizations. Understanding insider activities leads to an effective improvement in identifying insider attacks and limits their threats. This dissertation presents three systems to detect insider threats effectively. The aim is to reduce the false negative rate (FNR), provide better dataset use, and reduce dimensionality and zero padding effects. The systems developed utilize deep learning techniques and are evaluated using the CERT 4.2 dataset. The dataset is analyzed and reformed so that each row represents a variable length sample of user activities. Two data representations are implemented to model extracted features …

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